A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector Machine Classification

Abstract

Abstract Sensor nodes are tiny low-cost devices, prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector Machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then we used SVM to classify faulty readings from normal reading. After classification, faulty readings are discarded. We used network simulator NS-2.35 and Matlab for evaluation of our proposed method. We evaluated our fault detection algorithm using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC).